In [8]:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import pickle
import cv2
import glob
import time
import os
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label
%matplotlib inline
Reading and Displaying Images to train with¶
In [3]:
# Function to read in images.
def readImages(dir, pattern):
"""
Returns an image list with the image contained on the directory `dir` matching the `pattern`.
"""
images = []
for dirpath, dirnames, filenames in os.walk(dir):
for dirname in dirnames:
images.append(glob.glob(dir + '/' + dirname + '/' + pattern))
flatten = [item for sublist in images for item in sublist]
return list(map(lambda img: cv2.cvtColor(cv2.imread(img), cv2.COLOR_BGR2RGB), flatten))
vehicles = readImages('./training_data/vehicles', '*.png')
non_vehicles = readImages('./training_data/non-vehicles', '*.png')
Example of vehicle and non-vehicle image¶
In [4]:
index = 10
vehicle = vehicles[index]
non_vehicle = non_vehicles[index]
fig, axes = plt.subplots(ncols=2, figsize=(10, 10))
axes[0].imshow(vehicle)
axes[0].set_title('Vehicle')
axes[1].imshow(non_vehicle)
axes[1].set_title('Non-Vehicle')
print('Vehicle train image count: {}'.format(len(vehicles)))
print('Non-vehicle train image count: {}'.format(len(non_vehicles)))
In [5]:
# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
block_norm= 'L2-Hys',
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
block_norm= 'L2-Hys',
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
# Define a function to compute binned color features
def bin_spatial(img, size=(32, 32)):
# Use cv2.resize().ravel() to create the feature vector
features = cv2.resize(img, size).ravel()
# Return the feature vector
return features
# Define a function to compute color histogram features
# NEED TO CHANGE bins_range if reading .png files with mpimg!
def color_hist(img, nbins=32, bins_range=(0, 256)):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, parameters):
# apply color conversion if other than 'RGB'
if parameters.cspace != 'RGB':
if parameters.cspace == 'HSV':
feature_image = cv2.cvtColor(imgs, cv2.COLOR_RGB2HSV)
elif parameters.cspace == 'LUV':
feature_image = cv2.cvtColor(imgs, cv2.COLOR_RGB2LUV)
elif parameters.cspace == 'HLS':
feature_image = cv2.cvtColor(imgs, cv2.COLOR_RGB2HLS)
elif parameters.cspace == 'YUV':
feature_image = cv2.cvtColor(imgs, cv2.COLOR_RGB2YUV)
elif parameters.cspace == 'YCrCb':
feature_image = cv2.cvtColor(imgs, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(imgs)
# Call get_hog_features() with vis=False, feature_vec=True
if parameters.hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
parameters.orient, parameters.pix_per_cell, parameters.cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,parameters.hog_channel], parameters.orient,
parameters.pix_per_cell, parameters.cell_per_block, vis=False, feature_vec=True)
# Apply bin_spatial() to get spatial color features
spatial_features = bin_spatial(feature_image, parameters.size)
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=parameters.hist_bins, bins_range=parameters.hist_range)
return np.concatenate((spatial_features, hist_features, hog_features))
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
In [6]:
# Define parameters for feature extraction
class FeaturesParameters():
def __init__(self):
# HOG parameters
self.cspace = 'YCrCb'
self.orient = 8
self.pix_per_cell = 8
self.cell_per_block = 2
self.hog_channel = 'ALL'
# Bin spatial parameters
self.size = (16, 16)
# Histogram parameters
self.hist_bins = 32
self.hist_range = (0, 256)
def fitModel( cars, notcars, parameters):
car_features = list(map(lambda img: extract_features(img, parameters), cars))
notcar_features = list(map(lambda img: extract_features(img, parameters), notcars))
X = np.vstack((car_features, notcar_features)).astype(np.float64)
X_scaler = StandardScaler().fit(X) # Fit a per-column scaler
scaled_X = X_scaler.transform(X) # Apply the scaler to X
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features)))) # Define the labels vector
# Split up data into randomized training and test setsrand_state = np.random.randint(0, 100)
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.2, random_state=rand_state)
print('Vector length of Features: {}'.format(len(X_train[0])))
svc = LinearSVC()
time_start = time.time()
svc.fit(X_train, y_train)
time_end = time.time()
print('It took {} seconds to train SVC'.format(round(time_end-time_start, 2)))
print('SVC Test Accuracy: {} '.format(round(svc.score(X_test, y_test), 4))) # Check the score of the SVC
return (svc, X_scaler)
In [7]:
print("okay")
parameters = FeaturesParameters()
svc, scaler = fitModel(vehicles, non_vehicles, parameters)
Showimg examples of HOG images in three channels¶
In [8]:
img_cspaced_vehicle = cv2.cvtColor(vehicle, cv2.COLOR_RGB2YCrCb)
img_cspaced_non_vehicle = cv2.cvtColor(non_vehicle, cv2.COLOR_RGB2YCrCb)
_, hog_y_vehicle = get_hog_features(img_cspaced_vehicle[:,:,0], parameters.orient, parameters.pix_per_cell, parameters.cell_per_block,
vis=True, feature_vec=True)
_, hog_Cr_vehicle = get_hog_features(img_cspaced_vehicle[:,:,1], parameters.orient, parameters.pix_per_cell, parameters.cell_per_block,
vis=True, feature_vec=True)
_, hog_Cb_vehicle = get_hog_features(img_cspaced_vehicle[:,:,2], parameters.orient, parameters.pix_per_cell, parameters.cell_per_block,
vis=True, feature_vec=True)
_, hog_y_non_vehicle = get_hog_features(img_cspaced_non_vehicle[:,:,0], parameters.orient, parameters.pix_per_cell, parameters.cell_per_block,
vis=True, feature_vec=True)
_, hog_Cr_non_vehicle = get_hog_features(img_cspaced_non_vehicle[:,:,1], parameters.orient, parameters.pix_per_cell, parameters.cell_per_block,
vis=True, feature_vec=True)
_, hog_Cb_non_vehicle = get_hog_features(img_cspaced_non_vehicle[:,:,2], parameters.orient, parameters.pix_per_cell, parameters.cell_per_block,
vis=True, feature_vec=True)
fig, axes = plt.subplots(ncols=4, figsize=(15,15))
axes[0].imshow(vehicle)
axes[0].set_title('Vehicle')
axes[1].imshow(hog_y_vehicle, cmap='gray')
axes[1].set_title('HOG - Y')
axes[2].imshow(hog_Cr_vehicle, cmap='gray')
axes[2].set_title('HOG - Cr')
axes[3].imshow(hog_Cb_vehicle, cmap='gray')
axes[3].set_title('HOG - Cb')
fig, axes = plt.subplots(ncols=4, figsize=(15,15))
axes[0].imshow(non_vehicle)
axes[0].set_title('Non-Vehicle')
axes[1].imshow(hog_y_non_vehicle, cmap='gray')
axes[1].set_title('HOG - Y')
axes[2].imshow(hog_Cr_non_vehicle, cmap='gray')
axes[2].set_title('HOG - Cr')
axes[3].imshow(hog_Cb_non_vehicle, cmap='gray')
axes[3].set_title('HOG - Cb')
Out[8]:
In [9]:
def CarWindowSearch(img, clf, scaler, parameters, y_start_stop=[360, 700], xy_window=(64, 64), xy_overlap=(0.85, 0.85) ):
car_windows = []
windows = slide_window(img, y_start_stop=y_start_stop, xy_window=xy_window, xy_overlap=xy_overlap)
for window in windows:
img_window = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
features = extract_features(img_window, parameters)
scaled_features = scaler.transform(features.reshape(1, -1))
pred = clf.predict(scaled_features)
if pred == 1:
car_windows.append(window)
return car_windows
def CarDrawing(img, windows):
"""
Draw the `windows` on the image `img`.
"""
output = np.copy(img)
return draw_boxes(output, windows)
In [10]:
test_images = list(map(lambda img: cv2.cvtColor(cv2.imread(img), cv2.COLOR_BGR2RGB), glob.glob('./test_images/*.jpg')))
car_on_test = list(map(lambda img: CarDrawing(img, CarWindowSearch(img, svc, scaler, parameters)), test_images))
Function to Display Images¶
In [11]:
def DisplayImages(images, columns = 1, rows = 6, figsize=(30,30)):
"""
Display `images` on a [`columns`, `rows`] subplot grid.
"""
length = len(images)
fig, axes = plt.subplots(rows, columns, figsize=figsize)
indexes = range(columns * rows)
for ax, index in zip(axes.flat, indexes):
if index < length:
image = images[index]
ax.imshow(image)
DisplayImages(car_on_test)
Applying Heat and Thresholding Functions While Also Applying Lables¶
In [12]:
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
def HeatAndLabelsOnCars(img, boxes, threshHold = 9):
"""
Draw the car boxes `boxes` on the image `img` using a heatmap with threshold `threshHold`.
"""
# Add heat to each box in box list
heatmap = add_heat(np.zeros(img.shape), boxes)
# Apply threshold to help remove false positives
heatmap = apply_threshold(heatmap, threshHold)
# Visualize the heatmap when displaying
heatmap = np.clip(heatmap, 0, 255)
labels = label(heatmap)
return draw_labeled_bboxes(np.copy(img), labels)
heatlabels = list(map(lambda img: HeatAndLabelsOnCars(img, CarWindowSearch(img, svc, scaler, parameters)), test_images))
DisplayImages(heatlabels)
In [13]:
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, svc, scaler, parameters, y_start_stop=[350, 656], scale=1.5 ):
# Image color space changes
# apply color conversion if other than 'RGB'
if parameters.cspace != 'RGB':
if parameters.cspace == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif parameters.cspace == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif parameters.cspace == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif parameters.cspace == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif parameters.cspace == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
ystart, ystop = y_start_stop
ctrans_tosearch = feature_image[ystart:ystop,:,:]
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // parameters.pix_per_cell) - parameters.cell_per_block + 1
nyblocks = (ch1.shape[0] // parameters.pix_per_cell) - parameters.cell_per_block + 1
nfeat_per_block = parameters.orient*parameters.cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // parameters.pix_per_cell) - parameters.cell_per_block + 1
cells_per_step = 1 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step + 1
nysteps = (nyblocks - nblocks_per_window) // cells_per_step + 1
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, parameters.orient, parameters.pix_per_cell, parameters.cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, parameters.orient, parameters.pix_per_cell, parameters.cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, parameters.orient, parameters.pix_per_cell, parameters.cell_per_block, feature_vec=False)
draw_img = []
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*parameters.pix_per_cell
ytop = ypos*parameters.pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=parameters.size)
hist_features = color_hist(subimg, nbins=parameters.hist_bins)
# Scale features and make a prediction
test_features = scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
#test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
draw_img.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))
return draw_img
In [14]:
findingcars = list(map(lambda img: find_cars(img, svc, scaler, parameters), test_images))
drawingcars = list(map(lambda BoxandCar: CarDrawing(BoxandCar[0], BoxandCar[1]), zip(test_images, findingcars)))
DisplayImages(drawingcars)
In [15]:
drawingcars = list(map(lambda BoxandCar: HeatAndLabelsOnCars(BoxandCar[0], BoxandCar[1], threshHold=1), zip(test_images, findingcars)))
DisplayImages(drawingcars)
Making Video output from video input¶
In [16]:
from moviepy.editor import VideoFileClip
from functools import reduce
class HeatHistory():
def __init__(self):
self.history = []
def processVideo(inputVideo, outputVideo, frames_to_remember=3, threshhold=1):
"""
Process the video `inputVideo` to find the cars and saves the video to `outputVideo`.
"""
history = HeatHistory()
def pipeline(img):
boxes = find_cars(img, svc, scaler, parameters)
img_shape = img.shape
heatmap = add_heat(np.zeros(img_shape), boxes)
if len(history.history) >= frames_to_remember:
history.history = history.history[1:]
history.history.append(heatmap)
heat_history = reduce(lambda h, acc: h + acc, history.history)/frames_to_remember
heatmap = apply_threshold(heat_history, threshhold)
labels = label(heatmap)
return draw_labeled_bboxes(np.copy(img), labels)
myclip = VideoFileClip(inputVideo)
output_video = myclip.fl_image(pipeline)
output_video.write_videofile(outputVideo, audio=False)
processVideo('project_video.mp4', 'project_output_video.mp4', threshhold=9)